import os, sys, traceback now_dir = os.getcwd() sys.path.append(now_dir) import PySimpleGUI as sg import sounddevice as sd import noisereduce as nr import numpy as np from fairseq import checkpoint_utils import librosa, torch, pyworld, faiss, time, threading import torch.nn.functional as F import torchaudio.transforms as tat import scipy.signal as signal # import matplotlib.pyplot as plt from infer_pack.models import SynthesizerTrnMs256NSFsid, SynthesizerTrnMs256NSFsid_nono from i18n import I18nAuto i18n = I18nAuto() device = torch.device("cuda" if torch.cuda.is_available() else "cpu") class RVC: def __init__( self, key, hubert_path, pth_path, index_path, npy_path, index_rate ) -> None: """ 初始化 """ try: self.f0_up_key = key self.time_step = 160 / 16000 * 1000 self.f0_min = 50 self.f0_max = 1100 self.f0_mel_min = 1127 * np.log(1 + self.f0_min / 700) self.f0_mel_max = 1127 * np.log(1 + self.f0_max / 700) self.sr = 16000 self.window = 160 if index_rate != 0: self.index = faiss.read_index(index_path) # self.big_npy = np.load(npy_path) self.big_npy = self.index.reconstruct_n(0, self.index.ntotal) print("index search enabled") self.index_rate = index_rate model_path = hubert_path print("load model(s) from {}".format(model_path)) models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task( [model_path], suffix="", ) self.model = models[0] self.model = self.model.to(device) self.model = self.model.half() self.model.eval() cpt = torch.load(pth_path, map_location="cpu") self.tgt_sr = cpt["config"][-1] cpt["config"][-3] = cpt["weight"]["emb_g.weight"].shape[0] # n_spk self.if_f0 = cpt.get("f0", 1) if self.if_f0 == 1: self.net_g = SynthesizerTrnMs256NSFsid(*cpt["config"], is_half=True) else: self.net_g = SynthesizerTrnMs256NSFsid_nono(*cpt["config"]) del self.net_g.enc_q print(self.net_g.load_state_dict(cpt["weight"], strict=False)) self.net_g.eval().to(device) self.net_g.half() except: print(traceback.format_exc()) def get_f0(self, x, f0_up_key, inp_f0=None): x_pad = 1 f0_min = 50 f0_max = 1100 f0_mel_min = 1127 * np.log(1 + f0_min / 700) f0_mel_max = 1127 * np.log(1 + f0_max / 700) f0, t = pyworld.harvest( x.astype(np.double), fs=self.sr, f0_ceil=f0_max, f0_floor=f0_min, frame_period=10, ) f0 = pyworld.stonemask(x.astype(np.double), f0, t, self.sr) f0 = signal.medfilt(f0, 3) f0 *= pow(2, f0_up_key / 12) # with open("test.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) tf0 = self.sr // self.window # 每秒f0点数 if inp_f0 is not None: delta_t = np.round( (inp_f0[:, 0].max() - inp_f0[:, 0].min()) * tf0 + 1 ).astype("int16") replace_f0 = np.interp( list(range(delta_t)), inp_f0[:, 0] * 100, inp_f0[:, 1] ) shape = f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)].shape[0] f0[x_pad * tf0 : x_pad * tf0 + len(replace_f0)] = replace_f0[:shape] # with open("test_opt.txt","w")as f:f.write("\n".join([str(i)for i in f0.tolist()])) f0bak = f0.copy() f0_mel = 1127 * np.log(1 + f0 / 700) f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / ( f0_mel_max - f0_mel_min ) + 1 f0_mel[f0_mel <= 1] = 1 f0_mel[f0_mel > 255] = 255 f0_coarse = np.rint(f0_mel).astype(np.int) return f0_coarse, f0bak # 1-0 def infer(self, feats: torch.Tensor) -> np.ndarray: """ 推理函数 """ audio = feats.clone().cpu().numpy() assert feats.dim() == 1, feats.dim() feats = feats.view(1, -1) padding_mask = torch.BoolTensor(feats.shape).fill_(False) inputs = { "source": feats.half().to(device), "padding_mask": padding_mask.to(device), "output_layer": 9, # layer 9 } torch.cuda.synchronize() with torch.no_grad(): logits = self.model.extract_features(**inputs) feats = self.model.final_proj(logits[0]) ####索引优化 if hasattr(self, "index") and hasattr(self, "big_npy") and self.index_rate != 0: npy = feats[0].cpu().numpy().astype("float32") # _, I = self.index.search(npy, 1) # npy = self.big_npy[I.squeeze()].astype("float16") score, ix = self.index.search(npy, k=8) weight = np.square(1 / score) weight /= weight.sum(axis=1, keepdims=True) npy = np.sum( self.big_npy[ix] * np.expand_dims(weight, axis=2), axis=1 ).astype("float16") feats = ( torch.from_numpy(npy).unsqueeze(0).to(device) * self.index_rate + (1 - self.index_rate) * feats ) else: print("index search FAIL or disabled") feats = F.interpolate(feats.permute(0, 2, 1), scale_factor=2).permute(0, 2, 1) torch.cuda.synchronize() print(feats.shape) if self.if_f0 == 1: pitch, pitchf = self.get_f0(audio, self.f0_up_key) p_len = min(feats.shape[1], 13000, pitch.shape[0]) # 太大了爆显存 else: pitch, pitchf = None, None p_len = min(feats.shape[1], 13000) # 太大了爆显存 torch.cuda.synchronize() # print(feats.shape,pitch.shape) feats = feats[:, :p_len, :] if self.if_f0 == 1: pitch = pitch[:p_len] pitchf = pitchf[:p_len] pitch = torch.LongTensor(pitch).unsqueeze(0).to(device) pitchf = torch.FloatTensor(pitchf).unsqueeze(0).to(device) p_len = torch.LongTensor([p_len]).to(device) ii = 0 # sid sid = torch.LongTensor([ii]).to(device) with torch.no_grad(): if self.if_f0 == 1: infered_audio = ( self.net_g.infer(feats, p_len, pitch, pitchf, sid)[0][0, 0] .data.cpu() .float() ) else: infered_audio = ( self.net_g.infer(feats, p_len, sid)[0][0, 0].data.cpu().float() ) torch.cuda.synchronize() return infered_audio class Config: def __init__(self) -> None: self.hubert_path: str = "" self.pth_path: str = "" self.index_path: str = "" self.npy_path: str = "" self.pitch: int = 12 self.samplerate: int = 44100 self.block_time: float = 1.0 # s self.buffer_num: int = 1 self.threhold: int = -30 self.crossfade_time: float = 0.08 self.extra_time: float = 0.04 self.I_noise_reduce = False self.O_noise_reduce = False self.index_rate = 0.3 class GUI: def __init__(self) -> None: self.config = Config() self.flag_vc = False self.launcher() def launcher(self): sg.theme("LightBlue3") input_devices, output_devices, _, _ = self.get_devices() layout = [ [ sg.Frame( title=i18n("加载模型"), layout=[ [ sg.Input(default_text="hubert_base.pt", key="hubert_path"), sg.FileBrowse(i18n("Hubert模型")), ], [ sg.Input(default_text="TEMP\\atri.pth", key="pth_path"), sg.FileBrowse(i18n("选择.pth文件")), ], [ sg.Input( default_text="TEMP\\added_IVF512_Flat_atri_baseline_src_feat.index", key="index_path", ), sg.FileBrowse(i18n("选择.index文件")), ], [ sg.Input( default_text="你不需要填写这个You don't need write this.", key="npy_path", ), sg.FileBrowse(i18n("选择.npy文件")), ], ], ) ], [ sg.Frame( layout=[ [ sg.Text(i18n("输入设备")), sg.Combo( input_devices, key="sg_input_device", default_value=input_devices[sd.default.device[0]], ), ], [ sg.Text(i18n("输出设备")), sg.Combo( output_devices, key="sg_output_device", default_value=output_devices[sd.default.device[1]], ), ], ], title=i18n("音频设备(请使用同种类驱动)"), ) ], [ sg.Frame( layout=[ [ sg.Text(i18n("响应阈值")), sg.Slider( range=(-60, 0), key="threhold", resolution=1, orientation="h", default_value=-30, ), ], [ sg.Text(i18n("音调设置")), sg.Slider( range=(-24, 24), key="pitch", resolution=1, orientation="h", default_value=12, ), ], [ sg.Text(i18n("Index Rate")), sg.Slider( range=(0.0, 1.0), key="index_rate", resolution=0.01, orientation="h", default_value=0.5, ), ], ], title=i18n("常规设置"), ), sg.Frame( layout=[ [ sg.Text(i18n("采样长度")), sg.Slider( range=(0.1, 3.0), key="block_time", resolution=0.1, orientation="h", default_value=1.0, ), ], [ sg.Text(i18n("淡入淡出长度")), sg.Slider( range=(0.01, 0.15), key="crossfade_length", resolution=0.01, orientation="h", default_value=0.08, ), ], [ sg.Text(i18n("额外推理时长")), sg.Slider( range=(0.05, 3.00), key="extra_time", resolution=0.01, orientation="h", default_value=0.05, ), ], [ sg.Checkbox(i18n("输入降噪"), key="I_noise_reduce"), sg.Checkbox(i18n("输出降噪"), key="O_noise_reduce"), ], ], title=i18n("性能设置"), ), ], [ sg.Button(i18n("开始音频转换"), key="start_vc"), sg.Button(i18n("停止音频转换"), key="stop_vc"), sg.Text(i18n("推理时间(ms):")), sg.Text("0", key="infer_time"), ], ] self.window = sg.Window("RVC - GUI", layout=layout) self.event_handler() def event_handler(self): while True: event, values = self.window.read() if event == sg.WINDOW_CLOSED: self.flag_vc = False exit() if event == "start_vc" and self.flag_vc == False: self.set_values(values) print(str(self.config.__dict__)) print("using_cuda:" + str(torch.cuda.is_available())) self.start_vc() if event == "stop_vc" and self.flag_vc == True: self.flag_vc = False def set_values(self, values): self.set_devices(values["sg_input_device"], values["sg_output_device"]) self.config.hubert_path = values["hubert_path"] self.config.pth_path = values["pth_path"] self.config.index_path = values["index_path"] self.config.npy_path = values["npy_path"] self.config.threhold = values["threhold"] self.config.pitch = values["pitch"] self.config.block_time = values["block_time"] self.config.crossfade_time = values["crossfade_length"] self.config.extra_time = values["extra_time"] self.config.I_noise_reduce = values["I_noise_reduce"] self.config.O_noise_reduce = values["O_noise_reduce"] self.config.index_rate = values["index_rate"] def start_vc(self): torch.cuda.empty_cache() self.flag_vc = True self.block_frame = int(self.config.block_time * self.config.samplerate) self.crossfade_frame = int(self.config.crossfade_time * self.config.samplerate) self.sola_search_frame = int(0.012 * self.config.samplerate) self.delay_frame = int(0.01 * self.config.samplerate) # 往前预留0.02s self.extra_frame = int(self.config.extra_time * self.config.samplerate) self.rvc = None self.rvc = RVC( self.config.pitch, self.config.hubert_path, self.config.pth_path, self.config.index_path, self.config.npy_path, self.config.index_rate, ) self.input_wav: np.ndarray = np.zeros( self.extra_frame + self.crossfade_frame + self.sola_search_frame + self.block_frame, dtype="float32", ) self.output_wav: torch.Tensor = torch.zeros( self.block_frame, device=device, dtype=torch.float32 ) self.sola_buffer: torch.Tensor = torch.zeros( self.crossfade_frame, device=device, dtype=torch.float32 ) self.fade_in_window: torch.Tensor = torch.linspace( 0.0, 1.0, steps=self.crossfade_frame, device=device, dtype=torch.float32 ) self.fade_out_window: torch.Tensor = 1 - self.fade_in_window self.resampler1 = tat.Resample( orig_freq=self.config.samplerate, new_freq=16000, dtype=torch.float32 ) self.resampler2 = tat.Resample( orig_freq=self.rvc.tgt_sr, new_freq=self.config.samplerate, dtype=torch.float32, ) thread_vc = threading.Thread(target=self.soundinput) thread_vc.start() def soundinput(self): """ 接受音频输入 """ with sd.Stream( callback=self.audio_callback, blocksize=self.block_frame, samplerate=self.config.samplerate, dtype="float32", ): while self.flag_vc: time.sleep(self.config.block_time) print("Audio block passed.") print("ENDing VC") def audio_callback( self, indata: np.ndarray, outdata: np.ndarray, frames, times, status ): """ 音频处理 """ start_time = time.perf_counter() indata = librosa.to_mono(indata.T) if self.config.I_noise_reduce: indata[:] = nr.reduce_noise(y=indata, sr=self.config.samplerate) """noise gate""" frame_length = 2048 hop_length = 1024 rms = librosa.feature.rms( y=indata, frame_length=frame_length, hop_length=hop_length ) db_threhold = librosa.amplitude_to_db(rms, ref=1.0)[0] < self.config.threhold # print(rms.shape,db.shape,db) for i in range(db_threhold.shape[0]): if db_threhold[i]: indata[i * hop_length : (i + 1) * hop_length] = 0 self.input_wav[:] = np.append(self.input_wav[self.block_frame :], indata) # infer print("input_wav:" + str(self.input_wav.shape)) # print('infered_wav:'+str(infer_wav.shape)) infer_wav: torch.Tensor = self.resampler2( self.rvc.infer(self.resampler1(torch.from_numpy(self.input_wav))) )[-self.crossfade_frame - self.sola_search_frame - self.block_frame :].to( device ) print("infer_wav:" + str(infer_wav.shape)) # SOLA algorithm from https://github.com/yxlllc/DDSP-SVC cor_nom = F.conv1d( infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame], self.sola_buffer[None, None, :], ) cor_den = torch.sqrt( F.conv1d( infer_wav[None, None, : self.crossfade_frame + self.sola_search_frame] ** 2, torch.ones(1, 1, self.crossfade_frame, device=device), ) + 1e-8 ) sola_offset = torch.argmax(cor_nom[0, 0] / cor_den[0, 0]) print("sola offset: " + str(int(sola_offset))) # crossfade self.output_wav[:] = infer_wav[sola_offset : sola_offset + self.block_frame] self.output_wav[: self.crossfade_frame] *= self.fade_in_window self.output_wav[: self.crossfade_frame] += self.sola_buffer[:] if sola_offset < self.sola_search_frame: self.sola_buffer[:] = ( infer_wav[ -self.sola_search_frame - self.crossfade_frame + sola_offset : -self.sola_search_frame + sola_offset ] * self.fade_out_window ) else: self.sola_buffer[:] = ( infer_wav[-self.crossfade_frame :] * self.fade_out_window ) if self.config.O_noise_reduce: outdata[:] = np.tile( nr.reduce_noise( y=self.output_wav[:].cpu().numpy(), sr=self.config.samplerate ), (2, 1), ).T else: outdata[:] = self.output_wav[:].repeat(2, 1).t().cpu().numpy() total_time = time.perf_counter() - start_time self.window["infer_time"].update(int(total_time * 1000)) print("infer time:" + str(total_time)) def get_devices(self, update: bool = True): """获取设备列表""" if update: sd._terminate() sd._initialize() devices = sd.query_devices() hostapis = sd.query_hostapis() for hostapi in hostapis: for device_idx in hostapi["devices"]: devices[device_idx]["hostapi_name"] = hostapi["name"] input_devices = [ f"{d['name']} ({d['hostapi_name']})" for d in devices if d["max_input_channels"] > 0 ] output_devices = [ f"{d['name']} ({d['hostapi_name']})" for d in devices if d["max_output_channels"] > 0 ] input_devices_indices = [ d["index"] for d in devices if d["max_input_channels"] > 0 ] output_devices_indices = [ d["index"] for d in devices if d["max_output_channels"] > 0 ] return ( input_devices, output_devices, input_devices_indices, output_devices_indices, ) def set_devices(self, input_device, output_device): """设置输出设备""" ( input_devices, output_devices, input_device_indices, output_device_indices, ) = self.get_devices() sd.default.device[0] = input_device_indices[input_devices.index(input_device)] sd.default.device[1] = output_device_indices[ output_devices.index(output_device) ] print("input device:" + str(sd.default.device[0]) + ":" + str(input_device)) print("output device:" + str(sd.default.device[1]) + ":" + str(output_device)) gui = GUI()